class Actor(BaseModel):
    name: str = Field(description="name of an actor")
    film_names: List[str] = Field(description="list of names of films they starred in")
        
actor_query = "Generate the filmography for a random actor."
parser = PydanticOutputParser(pydantic_object=Actor)
prompt = PromptTemplate(
    template="Answer the user query.\n{format_instructions}\n{query}\n",
    input_variables=["query"],
    partial_variables={"format_instructions": parser.get_format_instructions()}
)
_input = prompt.format_prompt(query=actor_query)
output = model(_input.to_string())
parser.parse(output)
Actor(name='Tom Hanks', film_names=['Forrest Gump', 'Saving Private Ryan', 'The Green Mile', 'Cast Away', 'Toy Story'])
Fixing Output Parsing Mistakes#
The above guardrail simply tries to parse the LLM response. If it does not parse correctly, then it errors.
But we can do other things besides throw errors. Specifically, we can pass the misformatted output, along with the formatted instructions, to the model and ask it to fix it.
For this example, we’ll use the above OutputParser. Here’s what happens if we pass it a result that does not comply with the schema:
misformatted = "{'name': 'Tom Hanks', 'film_names': ['Forrest Gump']}"
parser.parse(misformatted)
---------------------------------------------------------------------------
JSONDecodeError                           Traceback (most recent call last)
File ~/workplace/langchain/langchain/output_parsers/pydantic.py:23, in PydanticOutputParser.parse(self, text)
     22     json_str = match.group()
---> 23 json_object = json.loads(json_str)
     24 return self.pydantic_object.parse_obj(json_object)